Learning genetic epistasis using Bayesian network scoring criteria

<p>Abstract</p> <p>Background</p> <p>Gene-gene epistatic interactions likely play an important role in the genetic basis of many common diseases. Recently, machine-learning and data mining methods have been developed for learning epistatic relationships from data. A well-known combinatorial method that has been successfully applied for detecting epistasis is <it>Multifactor Dimensionality Reduction </it>(MDR). Jiang et al. created a combinatorial epistasis learning method called <it>BNMBL </it>to learn Bayesian network (BN) epistatic models. They compared BNMBL to MDR using simulated data sets. Each of these data sets was generated from a model that associates two SNPs with a disease and includes 18 unrelated SNPs. For each data set, BNMBL and MDR were used to score all 2-SNP models, and BNMBL learned significantly more correct models. In real data sets, we ordinarily do not know the number of SNPs that influence phenotype. BNMBL may not perform as well if we also scored models containing more than two SNPs. Furthermore, a number of other BN scoring criteria have been developed. They may detect epistatic interactions even better than BNMBL.</p> <p>Although BNs are a promising tool for learning epistatic relationships from data, we cannot confidently use them in this domain until we determine which scoring criteria work best or even well when we try learning the correct model without knowledge of the number of SNPs in that model.</p> <p>Results</p> <p>We evaluated the performance of 22 BN scoring criteria using 28,000 simulated data sets and a real Alzheimer's GWAS data set. Our results were surprising in that the Bayesian scoring criterion with large values of a hyperparameter called α performed best. This score performed better than other BN scoring criteria and MDR at <it>recall </it>using simulated data sets, at detecting the hardest-to-detect models using simulated data sets, and at substantiating previous results using the real Alzheimer's data set.</p> <p>Conclusions</p> <p>We conclude that representing epistatic interactions using BN models and scoring them using a BN scoring criterion holds promise for identifying epistatic genetic variants in data. In particular, the Bayesian scoring criterion with large values of a hyperparameter α appears more promising than a number of alternatives.</p

Transcranial Magnetic Stimulation Intensities in Cognitive Paradigms

BACKGROUND: Transcranial magnetic stimulation (TMS) has become an important experimental tool for exploring the brain's functional anatomy. As TMS interferes with neural activity, the hypothetical function of the stimulated area can thus be tested. One unresolved methodological issue in TMS experiments is the question of how to adequately calibrate stimulation intensities. The motor threshold (MT) is often taken as a reference for individually adapted stimulation intensities in TMS experiments, even if they do not involve the motor system. The aim of the present study was to evaluate whether it is reasonable to adjust stimulation intensities in each subject to the individual MT if prefrontal regions are stimulated prior to the performance of a cognitive paradigm. METHODS AND FINDINGS: Repetitive TMS (rTMS) was applied prior to a working memory task, either at the 'fixed' intensity of 40% maximum stimulator output (MSO), or individually adapted at 90% of the subject's MT. Stimulation was applied to a target region in the left posterior middle frontal gyrus (pMFG), as indicated by a functional magnetic resonance imaging (fMRI) localizer acquired beforehand, or to a control site (vertex). Results show that MT predicted the effect size after stimulating subjects with the fixed intensity (i.e., subjects with a low MT showed a greater behavioral effect). Nevertheless, the individual adaptation of intensities did not lead to stable effects. CONCLUSION: Therefore, we suggest assessing MT and account for it as a measure for general cortical TMS susceptibility, even if TMS is applied outside the motor domain

Anti-mannose binding lectin antibodies in sera of Japanese patients with systemic lupus erythematosus

Mannose-binding lectin (MBL) is a key element in innate immunity with functions and structure similar to that of complement C1q. It has been reported that MBL deficiency is associated with occurrence of systemic lupus erythematosus (SLE). We hypothesized that anti-MBL antibodies, if present, would affect the occurrence or disease course of SLE, by reduction of serum MBL levels, interference of MBL functions, or binding to MBL deposited on various tissues. To address this hypothesis, we measured the concentration of anti-MBL antibodies in sera of 111 Japanese SLE patients and 113 healthy volunteers by enzyme immunoassay. The titres of anti-MBL antibodies in SLE patients were significantly higher than those in healthy controls. When the mean + 2 standard deviations of controls was set as the cut off point, individuals with titres of anti-MBL antibodies above this level were significantly more frequent in SLE patients (9 patients) than in controls (2 persons). One SLE patient had an extremely high titre of this antibody. No associations of titres of anti-MBL antibodies and (i) genotypes of MBL gene, (ii) concentrations of serum MBL, or (iii) disease characteristics of SLE, were apparent. Thus, we have confirmed that anti-MBL antibodies are indeed present in sera of some patients with SLE, but the significance of these autoantibodies in the pathogenesis of SLE remains unclear